Five AI risks IT professionals should spot before deployment

The excitement of launching a new AI system can be intense, especially when months or years of work are about to go live. But, rushing into deployment without a thorough risk review is a shortcut to potential disaster.

Poorly governed AI can harm reputations, break laws and erode public trust before it ever delivers value. This checklist focuses on five core risks to identify and address before moving from the lab into the real world.

Manage these early to strengthen outcomes, protect stakeholders and demonstrate high standards of ethical assurance.

One: bias in data and algorithms

Bias is not only a social issue; it is also a personal one. It poses a technical and reputational risk that can lead to unfair outcomes, discrimination and regulatory scrutiny. The roots are usually familiar: training data underrepresents key user groups, historical data bakes in historic inequalities, and models produce uneven error rates for different demographics. Proactive governance is the counterweight. Gartner’s AI TRiSM guidance frames bias, fairness and reliability as core controls that must be designed into the lifecycle and then continuously monitored. 

Validation must move beyond accuracy to cover fairness, explainability and robustness. KPMG’s model validation approach emphasises bias testing, challenger models and documented trade-offs, allowing decisions to be explained to auditors and regulators. EY’s responsible AI principles place accountability and transparency at the centre, which helps product teams capture why particular fairness choices were made, when and by whom. Make this routine. Treat fairness checks as a recurring control, not a one-off task before go-live.

Operationally, teams benefit from an operating model that strikes a balance between control and speed. Forrester describes a product mindset for data and AI that embeds governance into development without freezing innovation. In contrast, its Data and AI Governance Model targets outcomes such as security, privacy, and compliance alongside discovery and self-service.

Two: security vulnerabilities in AI systems

Every AI system inherits risks from its infrastructure and adds new ones. Adversarial attacks, data poisoning, model inversion and prompt injection are now routine parts of the threat landscape. Accenture’s 2025 resilience study reports that many organisations lack foundational AI security practices, while adversaries exploit model-specific weaknesses. Booz Allen details practical defences, from adversarial training and red teaming to privacy techniques that blunt reconstruction attacks. Build these into your standard reviews, then test as if an attacker is watching.

Exposure often spikes when models are exposed via public APIs or integrated agents. PwC’s analysis of agentic AI highlights how autonomous workflows widen the blast radius of prompt injection and data leakage, which raises the bar for identity, access controls and continuous monitoring. Accenture identifies data poisoning, model theft and adversarial attacks as top priority threats, advocating for AI asset inventories, red teaming and AI-specific threat intelligence. Treat AI systems like crown jewels: limit privileges, add rate limiting and ring fence training data and endpoints. 

Independent testing matters. Accenture emphasises the importance of continuous monitoring and third-party testing of models, backed by robust third-party risk management, to detect poisoning and manipulation early. Booz Allen’s playbooks highlight AI observability and attack pattern awareness as day-one capabilities. Bake these into change gates rather than waiting for a post incident retrofit. 

Three: lack of explainability and transparency

Opaque systems don’t naturally engender trust; they’re difficult to govern and risky in regulated sectors. This means we should design for interpretability up front. Gartner’s TRiSM model https://www.gartner.com/en/articles/ai-trust-and-ai-risk treats interpretability and privacy as design-time features, not documentation chores. KPMG recommends traceable decision pathways and challenger models to verify that explanations line up with outcomes. These practices increase adoption and reduce the probability of regulatory pushback.

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Clarity on who owns decisions also matters. EY’s principles put accountability on a firm footing, requiring clear ownership, accessible explanations, and channels for challenge and redress. BCG’s oversight guidance finds that successful scaling depends mainly on people and processes, including metrics and incentives that allow error escalation rather than concealment. In practice, this means incorporating explainability requirements into governance checklists, providing plain-language summaries for non-specialists, and training product owners who will be asked to defend outcomes.

Four: regulatory non-compliance and weak governance

AI deployment now operates within a rapidly evolving legal environment. The EU AI Act came into force on 1 August 2024 and will become fully applicable two years later in August 2026, with some exceptions. It employs a risk based approach, ranging from bans on unacceptable practices to stringent obligations for high risk systems, encompassing governance, technical documentation, human oversight and transparency. Deloitte’s brief and its deeper analysis lay out these classifications and compliance duties in practical terms. If you plan to deploy in the EU, assume that documentation, risk management and oversight will be subject to inspection.

Governance must be systematic rather than episodic. Gartner advises aligning people and technology from the start, inventorying all AI applications, classifying data, and enforcing policies through integrated controls. Forrester expects rapid growth in AI governance tooling as firms navigate both known and emerging risks, which signals that ad hoc spreadsheets and scattered approvals will not scale effectively. Establish a cross-functional governance body, standardise risk assessment and tie model release to compliance sign off.

Five: model drift and operational failure

Performance rarely stands still. Data distributions change. User behaviour evolves. Integration points break. Drift turns yesterday’s reliable model into today’s liability. McKinsey recommends a catalogue of AI risks and a prioritised mitigation plan, with legal, risk and technology teams embedded from design through to operation. Its work on intelligent automation underscores the need for clear roles, resilient processes and continuous validation to maintain stable services as models and data evolve.

Controls must operate reliably in production, not just in the laboratory. KPMG emphasises the importance of monitoring for data quality, drift, bias and failure modes as a core validation step, ensuring end-to-end traceability that keeps you audit-ready. BCG’s risk and compliance guidance points to human-in-the-loop escalation and risk-based decision models that adapt to changing conditions. Together, these patterns reduce silent degradation and speed recovery when behaviour shifts. KPMG’s framework and BCG’s risk management playbook map these controls to real operating models. 

Security and reliability intersect here. Accenture warns that, without continuous monitoring and independent testing, organisations remain exposed to data poisoning and model manipulation that may masquerade as drift. McKinsey’s operations work adds that fragmented ownership across implementation, risk, and process teams creates blind spots that slow response. Bring operations, security, and risk into a single runbook with clear thresholds, rollback plans, and on-call ownership. Accenture’s testing recommendations align well with this approach, as do McKinsey’s operating guidelines. 

What to do next

Begin by creating an inventory of models within scope for deployment. Map each to the five risks above and record existing controls and gaps. Align the plan with your legal and compliance obligations, especially if the EU AI Act applies. Add bias testing, adversarial security reviews, explainability criteria, governance sign-off, and production monitoring to your release checklist. The goal is not bureaucracy. The goal is to make AI reliable, lawful, and defensible before it meets real users. Gartner’s TRiSM, Deloitte’s EU AI Act guidance , and McKinsey’s risk frameworks provide practical reference points that leadership teams can apply today.